Silo Thinking

By now, you may have figured out that my last few blogs were inspired by the recent book Thinking, Fast and Slow of Professor Daniel Kahneman, a trained psychologist and a co-recipient of 2002 Nobel Prize in Economics.  The Prize citation recognized him "for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty."  

Along with many researchers and thinkers, Kaheman has been making significant contributions to the field of what is now known as Behavioral Economics that “study the effects of social, cognitive and emotional factors on the economic decisions of individuals and institutions and the consequences for market prices, returns and the resource allocation.”  

You may wonder if main stream economists did not incorporate the effects of social, cognitive and emotional factors, what was the economic decision theory like and how good are they?  In his book, Professor Kahneman recalls how he got involved in the study of decision making.  It all started one day in early 1970s when late Amos Tversky, Kahneman’s collaborator and close friend, showed him an essay that started with the sentence “The agent of economic theory is rational, selfish, and his tastes do not change.”  The statement is a concise summary of one key assumption of the dominant neoclassical economics theory and rationality in this context means “an individual acts as if balancing costs against benefits to arrive at action that maximizes personal advantage.”

What is interesting is the ensuing “what?!” moment.  As Kahneman recalls “To a psychologist, it is self-evident that people are neither fully rational nor completely selfish, and that their tastes are anything but stable.  Our two disciplines seemed to be studying different species....”  A Nobel prize-worthy work was then begun.  Indeed this story is a good illustration of why honest inter-disciplinary efforts can be very productive that bring different perspectives and thus connect otherwise isolated frames of mind.

In subsequent decades, Kahneman and other researchers have brought further insights from psychology and injected more reality into economics theory.  Behavioral economics is now a significant scientific discipline that offered more satisfactory explanations and predictions of human economic behaviors.  

One example Kahneman used in his book to illustrate the inadequacy of classical models is fairly easy to follow.  If utility of wealth is indeed the deciding factor as the classical theory postulated, then the following two games would produce similar preferences.  But intuitively, you know it cannot possibly be the case.  

1.      In addition to whatever you own, you have been given $1,000.  You are now asked to choose one of these options: 50% chance to win $1,000 OR get $500 for sure.

2.      In addition to whatever you own, you have been given $2,000.  You are now asked to choose one of these options: 50% chance to lose $1,000 OR lose $500 for sure.

With a 20/20 hindsight, you may wonder why did it take so long for experts to amend flawed models and why so many experts were oblivious to the obviously (to common men) erroneous implications when applying some methods of classical decision theory to scenarios involving losses and risks.

Economics is certainly not the only field that suffers from the ill effects of silo thinking. Researchers in all fields are often so absorbed and entrenched in their research and are so comfortable with their training, they accept the theory and rarely bother to revisit the critical assumptions.  Another distinct possibility is that some theories were never really applied in practice nor produced any serious consequences; thus there is no incentive to validate it.  Worse yet, it is far easier to make incremental enhancements to accepted theory than to establish something fundamentally new.  

In fact silo thinking is a systematic problem that does not end at scientific explorations. It is prevalent in organization and business of all kinds as well.  A vast majority of us devote most of our energy in digging inch by inch the very silo we live in that provides us a comfortable “shelter”, the same reason why most researchers don’t branch out from their familiar ground.  

Whenever one goes out of his or her own silo once in a while, good things happen.  At a minimum, it could be refreshing to see different perspectives.  Sometimes, one may even enjoy an Eureka moment like ancient Greek scholar Archimedes did or a sudden enlightenment like Zen Buddhism calls it.  As trained psychologists, Kahenman and Tversky recognized the importance of the psychological value of gains and losses in decision making that was missed by main stream economists.  Their introduction of the concept of loss aversion has since been considered the most significant contributions of psychology to behavioral economics.  Sounds pretty intuitive and easy, isn’t it?  You can make a difference too if you think out of box once in a while!

Talk to you soon!  Sign off by this frog at this well on this day.

Our Causal Mind

Causal (not to be confused with “casual”) is a word that is not often used or heard in our daily life.  Yet the word describes a concept that is deeply imbedded in our minds and influences how you and I behave all the time.  Looking up the dictionary, you will find that the word causal means ”relating to or arising from a cause” and the noun causality (and causation) is about the cause-effect relationship of events.  That is, these words describe the familiar notion that one event (the effect) is a consequence of another event or set of events (the cause). 

 

Don’t we all resist the idea that things/events can happen with no reasons?  Don’t we all like to believe that if we had known every piece of information and actions, then we could have predicted where we would have been?  By the same token but going backward in time, don’t we also naturally feel that given an outcome, it would be possible (at least in principle) to identify what have caused it?   Unfortunately, such feelings and beliefs, while comforting, may not always be correct and sometimes are outright illusionary.  But, since it is practically impossible to replay alternate path completed with all the factors minus the causes, we happily convince ourselves to accept the universality of causation anyway even when it is not certain or true. 

Narrative fallacy (see the book The Black Swan by Nassim Taleb) is a consequence of our causal minds.  We love stories.  Journalists report and write stories of the news events.  In the process of making sense of the events and happenings, we willingly become victims of our “Illusion of Understanding”.  What makes it worse is that according to Professor Daniel Kahneman discusses in his recent book Thinking, Fast and Slow, we also have a remarkable ability in sense-making.  As we attempt to make sense of the world, we usually reconstruct a coherent story after the fact and make sure it is consistent with the outcome.  This is done regardless if one had different beliefs or predictions and psychologists call it hindsight or the outcome bias.   When talking to your aging parents, haven’t you ever wondered where did they get their distorted stories from?  Have you ever realized that one day it is where you will end up to be at too? 

What about experts’ opinions?  Consider the daily headlines of the financial market and sports news.  We are always told reasons why the stock market went up or down and why a pro sport team won or lose, after the fact.   Perhaps we can sympathize with pundits and commentators who are subject to tremendous pressure that they need to explain and predict things for which they are supposed to be the experts.  But professional stock pickers, traders, company CEOs and political leaders are not exempted from this problem either.  “Illusion of skills” and downplaying “luck” (or more accurately, chance) is necessary and natural for those who eager to justify their existence and rewards.  We the consumers of these “goods” certainly play a huge role as we provide the insatiable demand for such behaviors.

The truth is that our minds just love and yearn for causal explanations.  Statistics is a useful tool but we don’t seem to handle statistical information and complexity well.  We (well-educated ones included) routinely overlook and ignore the sound advice and logic that CORRELATION DOES NOT IMPLY CAUSATION.  Many glaring traps and examples can be found the health related areas.  When was the last time you heard a claim, a report, or controversy about what may help with an illness or improve your heath?  When was the last time you received tempting information about certain diet supplements or therapy that will make you healthy?  More often than not, such information are either interpreted incorrectly due to ignorance or simply designed to prey on our tendency of substituting causation for correlation, hoping by chance the cure will turn into certainty.

What tops all among our inadequacies is perhaps what Donald Rumsfeld, the former Secretary of Defense, referred to as “there are things we do not know we don’t know” or for short “unknown unknowns” (when he addressed the intelligence challenges early 2002, prior to the 2003 invasion of Iraq ).  This is related to the phenomenon what Professor Kahneman called WYSIATI – What You See Is All There.  It is simply impossible to take into account of information that does not even come to your mind because you never knew it is there to begin with!   One trivial consequence of this flaw is we rarely recognize or reward those tremendous efforts that prevented a disaster from happening.  Instead we celebrate heroism in a disaster all the time which is simply the mirror opposite of the former.  Sadly, out of the four quadrants including known knowns, known unknowns, unknown knowns, the quadrant of unknown unknowns is for sure the largest although we will never know how big it really is.

In sum, we misconstrue noisy data and confuse complex, non-sequential events with simple, plausible, time-ordered, causal explanations.  Satisfied with our story, we congratulate ourselves and gladly move on with confidence.  Perhaps such optimism is necessary for evolutionary reasons and survival.  In his recent book, Professor Daniel Kahneman also tells us that our brains are “wired” as two counterbalancing systems feeding and driving each other: a Fast System that is instinctive and responds instantly to received stimuli based on a model; and a Slow System which is deliberate and analytic in digesting the experiences and updating the model used by the Fast System.  The model used by our fast and intuitive system appears to have all the characteristics of a causal one.

Further, our Slow System thinks and pursues answers to the questions of whys and tries to make sense of the world “off-line”.  While it is slow and tend to be lazy (or has a limited energy?), we are lucky that as a social-economic animal, we do share the results and discoveries, and learn from each other.   The most familiar examples can be found in fields of science, engineering and technology.   We wouldn’t be where we are if it weren’t because many have devoted so much of their energy to pursue relentlessly the answers to what’s, why’s and how’s.  

Science is definitely not the only way we make sense of the world.  Spiritual and philosophical approaches are far more popular and prevalent in the history of mankind.  Confronting the difficulty of having satisfactory explanations and the impossibility of knowing and predicting accurately, religion offers a comforting solution.   To begin with, one can find his/her answers by attributing all unexplainables (and sometimes explainables as well) to some supernatural entities (gods).   Abrahamic religions for instance (Judaism, Christianity, and Islam) believe in omnipotence that god is in control of everything.  Some take a step further and believe in omnicausality that god is the cause of all things.  Indian religions (Hinduism, Buddhism included) on the other hand, discuss the concept of Karma extensively which is about the cycles of cause-effects.  All these demonstrate just how strong the desire is for a causal model of the world by human.  

Are you as surprised as I am that despite all the flaws and shortcomings, we seem to be doing ok so far?   But do you really think our causal mind is the best design?  

Talk to you soon!

How Useful is Additional Information?

As an informed and learned man, we are exposed to large amount of data and information all the time. Some of these data are incomplete, noisy; some are confusing and contradicting, and some are misleading or downright wrong.  Over time, with some filtering and learning, we formed our own (biased) opinions, and apply our judgment to event of interest.  While the intent and process may seem innocuous, our biases could and do sometimes have unintended and/or horrific consequences.  Take the recent news from Sanford, Florida as an example.  Trayvon Martin, a 17 years old, unarmed teen was shot dead by a neighborhood watch volunteer George Zimmerman.  While many details and facts are still unavailable to public, we do know that the tragic event started when George Zimmerman, according to his account, saw the hooded teen in the neighborhood and thought “he looks suspicious”.  

In his recent book Thinking, Fast and Slow, Professor Daniel Kahneman, 2002 Nobel Laureate in Economics, discussed how our brains struggle to balance our quick intuitions and slow analytics when making judgment.   We are proud of our build-in survival instincts and life experience that helped our quick intuitive responses.  We insist, while acknowledging such responses are not always correct, they are time tested and often sound.  We are perfectly ok to have such an efficient way of cutting through complex reality to get to the guts of the resolution.  And we can’t imagine how we can survive or live without such ability.  On the flip side, words such as profiling, stereotyping, bias, prejudice are often used to describe the same intuitive conclusions.   As we live in this information age and are constantly bombarded with noisy information and news, one must wonder how good is our gut reactions and how wrong could they be.  

In the chapter “Causes trump Statistics” of the said book, Professor Kahneman gave the following example:  “A cab was involved in a hit-and-run accident at night.  Two cab companies, the Green and the Blue, operate in the city.  You are given the following data: 85% of the cabs in the city are Green and 15% are blue.  A witness identified the cab as Blue.  The court tested the reliability of the witness under the circumstances that existed on the night of the accident and concluded that the witness correctly identified each one of the two colors 80% of the time and failed 20% of the time.”  Then Professor Kahneman asked us to answer intuitively to the question “What is the probability that the cab involved in the accident was Blue rather than Green?”

I don’t know about you.  But if I have to give an answer intuitively, I would probably put my bet on the taxi involved is Blue given the seemingly pretty solid witness’ account.  In real life, each of us runs into this type of situations from time to time and offers our guess freely.  But what if the answer matters (e.g., you are a juror in such a case in court) and what if we are wrong?  

So, what is the odd that I would win my bet in absence of any further information?  In this example, we were first given the fact that only 15% of taxi in this city is Blue.   If we knew nothing else, most of us probably would agree that a reasonable guess would be there is a 15% chance that it is a Blue taxi when an accident involving a taxi occurs.   The challenge comes when we are given further and additional information since we believe we now know a lot more about the event.  

In this example, we are told that there is an eye witness who said the taxi involved is Blue.   After all, the witness is 80% reliable that seems pretty good.  In a different setup, we might be told that Blue taxi is much more prune to having accident based on historical data, etc.   The core issue remains the same however: how can we incorporate such additional information properly?

More than 300 years ago, the 18^th century English mathematician Thomas Bayes addressed this very issue and developed an approach of how one could update one’s beliefs when given new evidence.   His work is fundamental to the theory of probability and statistics and bears names like Bayes’ Theorem, Bayes’ law, Bayesian Statistics, and Bayesian inference, etc.   If we applies Bayes’ Theorem to our taxi problem as Professor Kahneman suggested, we could update our estimate as follows once we are told the 2^nd piece of information that there is an eye witness who identified the taxi is Blue.  

The calculation is based on the simple observation that the probability of {the taxi is Blue} and {eye witness thinks it is Blue} can be expressed in two ways with priori or posterior probabilities:  this probability is equal to the product of the posterior probability of {that taxi is Blue, given eyewitness thinks it is Blue} of interest and the probability of {eye witness thinks it is Blue}.  It can also be expressed as the product of the probability of {eyewitness thinks it is Blue, given taxi is Blue} which is 0.8 and the priori probability of {that taxi is Blue} which is 0.15.  

But the eyewitness could be wrong.  The eyewitness can be thinking that the taxi is Blue when it is actually Green and he mistaken it to be Blue (20% chance), or taxi is indeed Blue and he got it right which is 80%.  Thus the latter probability of the evidence that eyewitness think it is blue is thus 0.20x0.85 + 0.80x0.15, or 0.29.  Therefore the posterior probability of our interest that the taxi is indeed Blue given eyewitness thinks it is Blue would be the ratio of 0.80x0.15 over 0.29, or 41%.  In other words, with the additional evidence provided by the eyewitness, the chance that the taxi is blue has risen almost three folds from 15% (based on the priori statistic) to 41%.  However, the resulted probability is still smaller than the obvious reference figure of 50% from a random toss of a fair coin if you had absolutely no information other than the fact there are two colors of taxi in the city.

What the example illustrates is that the additional information (in this example, the eyewitness account) may not give us nearly as much as we thought it would.  That is, we tend to give too much significance and weight to such additional information than it really deserves.   For this example, to have the updated estimate that the taxi is Blue be better than 50% (a totally uninformed random guess), the eye witness needs to be more than 85% reliable.   Note if we want to be 95% sure, the eyewitness reliability needs to be better than 98% in this case.

You can easily create examples like this with other subjects and numbers.  Perhaps you are visiting an area and you were told in advance that the minority population there is 15%.   Perhaps you are visiting an area and you were told that a particular group of certain profile is known to be responsible for 80% of the crimes in that area.  When there is a crime reported and a witness identified the perpetrator is of minority race or fits the profile of that group.  What is the chance that crime was indeed committed by a person of minority race or of that group as eye witness claimed?

When George Zimmerman of Sanford thought Trayvon Martin looked suspicious, when he began to follow him with a hand gun in his waist, the encounter ended with a tragic shooting death of the teen.   When the law enforcement and intelligence agencies collect statistics and profile groups, when institutions collect statistics about college admission of minority or under-represented groups, shouldn’t we all be concerned about how the data are used and if there is a rush to judgment?  Shouldn’t we all be worried about making proper inferences when we pick up new knowledge? 

Talk to you soon!

Bully and Beef

In my last blog, I touched upon the Second Opium War of China vs. British Empire in late 19^th century.  Interestingly, one hot button issue in recent weeks from Taiwan is the Second Beef War – popular protest of Taiwan government’s attempt in lifting the ban of importing beefs that contains (any) residue of ractopamine 瘦肉精.   When asked in his interview earlier today with local media in Taiwan, “America doesn’t bully,” said William Stanton, Director of the AIT (American Institute in Taiwan, the official representative of U.S. government in Taiwan).   You might think the most powerful nation of the world wouldn’t need to bully a small country like Taiwan, or would it?

Hearing and reading news like that, one can’t help but to conjure up this image of an bull running into a china shop, knocking down and breaking any objects around it.  According to the Online Etymology Dictionary however, the origin of the word bully had nothing to do with bulls.  It came from 16^th century Dutch and German and until late 19^th century, it was used with a positive meaning of "sweetheart" "lover, brother".  But Taiwan people are not likely to consider recent statements and actions by Mr. Stanton and other American officials/politicians an expression of brotherly love.

According to the Wikipedia entry, “Ractopamine is a drug that is used as a feed additive to promote leanness in pigs raised for their meat.”  It is the active ingredient in products known as Paylean for pigs and Optaflexx for cattle which were approved by FDA for finishing feeds before slaughter in 1999 and 2003 respectively (and for turkey in 2009).  The drug is now used in about 45% of the U.S. pigs and 30% of cattle.   The problem is that with the current industrial meat production process and regulations in U.S., there is no way for consumers to identify which is which. The obvious motivation behind the push is an economic one:   additional drug sales for Elanco Animal Health, a division of Eli Lilly and Company, additional revenue and profits for pig and cattle farmers/companies from (a few percent more) efficiency and resulting additional lean meat.  And when the domestic market is covered, why not reach out to international markets to sale the products?   

The first Beef War started in Oct 2009 when President Ma’s government reached an agreement with U.S. of a protocol under which Taiwan would import “risky” beef and beef offal (with higher risks of being infected by mad cow disease) from U.S.  In the end, Ma’s Nationalist government could not get the protocol ratified inspite of its supermajority in the Legislative Branch.  The Food Safety Act of Taiwan was amended to allow  more varieties of beef be imported but continue to ban virtually the risky beef and beef offal.  Since then, it is widely believed that Washington has been stalling and holding hostage the Trade and Investment Framework Agreement (TIFA) negotiation with Taiwan.  It is an open secret that Taiwan government wants TIFA badly to prevent itself from falling further into China’s grip economically and from falling behind in its competition with ASEAN countries.   Note since the first Beef War, U.S. beef export to Taiwan has increased significantly - from 27 thousand metric tons in 2009 to about 39 thousand metric tons in 2010.  It went down to 35.4 thousand metric tons in 2011 because of the strict regulation and zero tolerance inspection of imported beef for ractopamine, so complained by U.S. representatives.

Of course, no one would be making a big fuss if there weren’t financial and political interests involved.  $200 million dollars (that is the total value of the 2011 export of beef to Taiwan) may sound small, considering the total U.S. export to Taiwan in 2011 was 26 billion dollars.  Nevertheless, a dollar is a dollar and in U.S., there are particular business and farmers who stand to gain from increased export.  There are powerful lobbyists and politicians who represent these constituents and have allies who scratch each other's back.  On Taiwan side, there are pig farmers who worry about the “slippery slope” with pork next on the wish list, the politicians who seize on the popular sentiment and nationalism, and those of the opposition parties who wouldn’t miss the opportunity to attack the ruling party.

Shall we then ask the experts and trust the science like President Ma and Mr. Stanton of AIT suggested?  There are several arguments offered by both that would lead to the obvious conclusion that it is safe to import U.S. beef with limited residue of ractopamine.  Unfortunately some of these arguments such as “FDA approved”, “it is safe for Americans to consume“ only deepened the suspicion and resentment of the American Exceptionalism.  

Arguments have also been made to “bring it back to science” and “trust the experts” which are less than convincing in this case since experts do not have consensus on the risks of consumption of ractopamine fed pigs and beef.  Although limited studies and absence of serious health threat reports appear to support the claim that meat with small residue of ractopamine is safe for human, science cannot be expected to prove it is risk free.  That brings us to the real and fundamental problem – psychologists have recognized for some time that human are not very good in dealing with very small risks; we either ignore them completely or give them too much weight than any data would support.  

In his recent book Thinking, Fast and Slow, Daniel Kahneman, 2002 Nobel Laureate in Economics, devoted a whole section on the conflicting values of the Public and the Experts when it comes down to the judgment of risks like the case with the ractopamine.  Professor Kahneman sketched the opposing views of two of his esteemed colleagues:  Psychologist Paul Slovic does not believe in the existence of objective risk although risks are real.  At the other end of the spectrum, Harvard Law Professor Cass Sunstein believes it is possible to isolate decision makers from public pressure and to trust impartial experts who would not be swayed by irrational and emotional responses of public.  

Professor Kahneman agreed wisely with both.  He had this to say: “Rational or not, fear is painful and debilitating, and policy makers must endeavor to protect the public from fear, not only from real dangers.”  He went on to say “Psychology should inform the design of risk policies that combine the experts’ knowledge with the public’s emotions and intuitions.”  President Ma must be kicking himself for not handling this delicate issue better at the beginning.  It remains to be seen if his government can dig itself out of the hole it is in.  One thing is clear though: science alone cannot solve political problems.

Talk to you soon!